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<p>Under low-light conditions, images and videos suffer compounded degradation, including loss of dark-region details, color shifts, and noise amplification. Existing methods are typically designed separately for images or videos, making it challenging to balance enhancement quality, temporal consistency, and efficiency in a single framework. This study proposes LIVE-Net, a lightweight down-up sampling backbone that unifies image and video low-light enhancement. Its core components include: (1) the Local Saliency-Aware PoolFormer block, which integrates SimAM for adaptive attention to dark-region outliers while achieving linear-complexity global aggregation via a parameter-free pooling token mixer; (2) the Selective Kernel Fusion module and the Grouped Shuffle Attention Fusion module, enabling content-adaptive detail-semantic trade-offs and robust cross-stage aggregation with minimal cost; (3) Depthwise Convolution Gated Recurrent Unit with a Temporal Difference Consistency loss for lightweight temporal modeling and flicker suppression without optical flow. With only 0.13M parameters and 1.48G FLOPs, LIVE-Net achieves 23.18 dB/0.868 and 26.37 dB/0.943 on LOL-v2 real and synthetic datasets, remains competitive on LOL-v1 and SID, and improves temporal consistency on DID and SDSD while maintaining single-frame quality, demonstrating its unified, robust, and real-time capabilities.</p>

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LIVE-Net: A unified lightweight network for low-light image and video enhancement with temporal consistency

  • 期刊论文

  • 发表日期：

    2026年5月20日

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  • Minghai Jiao,
  • Haoyang He,
  • Yixian Liu,
  • Wenyan Jiang,
  • Jiangang Hu,
  • Yuhuai Peng

摘要

Under low-light conditions, images and videos suffer compounded degradation, including loss of dark-region details, color shifts, and noise amplification. Existing methods are typically designed separately for images or videos, making it challenging to balance enhancement quality, temporal consistency, and efficiency in a single framework. This study proposes LIVE-Net, a lightweight down-up sampling backbone that unifies image and video low-light enhancement. Its core components include: (1) the Local Saliency-Aware PoolFormer block, which integrates SimAM for adaptive attention to dark-region outliers while achieving linear-complexity global aggregation via a parameter-free pooling token mixer; (2) the Selective Kernel Fusion module and the Grouped Shuffle Attention Fusion module, enabling content-adaptive detail-semantic trade-offs and robust cross-stage aggregation with minimal cost; (3) Depthwise Convolution Gated Recurrent Unit with a Temporal Difference Consistency loss for lightweight temporal modeling and flicker suppression without optical flow. With only 0.13M parameters and 1.48G FLOPs, LIVE-Net achieves 23.18 dB/0.868 and 26.37 dB/0.943 on LOL-v2 real and synthetic datasets, remains competitive on LOL-v1 and SID, and improves temporal consistency on DID and SDSD while maintaining single-frame quality, demonstrating its unified, robust, and real-time capabilities.